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2012年计算机应用与系统建模国际会议

作者： Sha Yi Huang Li Chu Jiafu Zhang Lili Northeastern University

Introducing a wavelet neural network model (WNNM) at a route maintenance stage to predict the position of nodes in ad hoc networks, a new weighted clustering algorithm (WCA) is presented. Using the nodes’ position information from GPS, this algorithm predicts the position of nodes at next time by WNNM, then calculates the time the neighbor nodes spend moving out of the coverage of cluster heads by the predicted values and takes it as a measurement of aggregate holding time. If the cluster structure tends to be unstable, a pre-repair mechanism will function before the link fails, thus avoiding frequent break of links and improving the network performance. Simulation results show that compared to the Lowest-ID WCA and location-based WCA, the algorithm proposed can increase the packet delivery rate by 9% and 6%, respectively, and decrease the numbers of link break by about 70% and 50%, respectively.

关键词： Ad hoc network Weighted clustering algorithm （WCA） Location prediction Wavelet neural network model （WNNM）

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A Location prediction Method Based on GA-LSTM Networks and Associated Movement Behavior Information

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Journal of Information Hiding and Privacy Protection 2020年第4期2卷 187-197页

作者： Xingxing Cao Liming Jiang Xiaoliang Wang Frank Jiang Hunan University of Science and Technology Xiangtan411201China Deakin University Geelong3216Australia

Due to the lack of consideration of movement behavior information other than time and location perception in current Location prediction methods,the movement characteristics of trajectory data cannot be well expressed,which in turn affects the accuracy of the prediction results.First,a new trajectory data expression method by associating the movement behavior information is given.The pre-association method is used to model the movement behavior information according to the individual movement behavior features and the group movement behavior features extracted from the trajectory sequence and the region.The movement behavior features based on pre-association may not always be the best for the prediction model.Therefore,through association analysis and importance analysis,the final association feature is selected from the pre-association features.The trajectory data is input into the LSTM networks after associated features and genetic algorithm(GA)is used to optimize the combination of the length of time window and the number of hidden layer nodes.The experimental results show that compared with the original trajectory data,the trajectory data associated with the movement behavior information helps to improve the accuracy of Location prediction.

关键词： Location prediction information association feature selection GA-LSTM

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A novel proactive soft load balancing framework for ultra dense network

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Digital Communications and Networks 2023年第3期9卷 788-796页

作者： Miaona Huang Jun Chen School of Electrical Engineering and Intelligentization Dongguan University of TechnologyDongguanGuangdong523808China Huawei Technology Shenzhen518129China

A major challenge for the future wireless network is to design the self-organizing architecture.The reactive self-organizing model of traditional networks needs to be transformed into an active self-organizing network.Due to the user mobility and the coverage of small cells,the network load often becomes unbalanced,resulting in poor network performance.Mobility management has become an important issue to ensure seamless communication when users move between cells,and proactive mobility management is one of the important functions of the active Self-Organizing Network(SON).This paper proposes a proactive mobility management framework for active SON,which transforms the original reactive load balancing into a forward-aware and proactive load balancing.The proposed framework firstly uses the BART model to predict the users’temporal and spatial mobility based on a weekly cycle and then formulate the MLB optimization problem based on the soft load.Two solutions are proposed to solve the above MLB problem.The simulation results show that the proposed method can better optimize the network performance and realize intelligent mobile management for the future network.

关键词： 5G and beyond Proactive load balancing Heterogeneous network Soft load Location prediction

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location-and Relation-Based Clustering on Privacy-Preserving Social Networks

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Tsinghua Science and Technology 2018年第4期23卷 453-462页

作者： Dan Yin Yiran Shen College of Computer Science and Technology Harbin Engineering University

Graph clustering has a long-standing problem in that it is difficult to identify all the groups of vertices that are cohesively connected along their internal edges but only sparsely connected along their external edges. Apart from structural information in social networks, the quality of the location-information clustering has been improved by identifying clusters in the graph that are closely connected and spatially compact. However, in real-world scenarios, the location information of some users may be unavailable for privacy reasons, which renders existing solutions ineffective. In this paper, we investigate the clustering problem of privacy-preserving social networks, and propose an algorithm that uses a prediction-and-clustering approach. First, the location of each invisible user is predicted with a probability distribution. Then, each user is iteratively assigned to different clusters. The experimental results verify the effectiveness and efficiency of our method, and our proposed algorithm exhibits high scalability on large social networks.

关键词： clustering Location prediction privacy-preserving social networks

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Predicting mobile users’behaviors and locations using dynamic Bayesian networks

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Journal of Management Analytics 2016年第3期3卷 191-205页

作者： Jianrong Hou Hui Zhao Xiaofeng Zhao Jie Zhang Antai College of Economics and Management Shanghai Jiao Tong UniversityShanghaiChina School of Computer Science and Software Engineering East China Normal UniversityShanghaiChina University of Mary Washington FredericksburgUSA

This paper studies the traveling Location prediction problem for detecting whether mobile users will leave their living area and where they will go.We investigate the hidden connections between users’behaviors in different locations and online social interactions.We combine dynamic Bayesian networks with a majority voting model which is based on social interaction information to estimate the users’behaviors and predict the locations.By analyzing Instagram media records,spanning a period of 3 months,we explore rarely visited locations,which are often ignored as noise in previous research.In comparison,our model,using Instagram data with two existing Location prediction models,shows that(1)our Location prediction is more accurate and robust in both the general location and the location outside the living area;(2)social relations are instrumental in the Location prediction as social interaction information can increase the accuracy of the prediction.

关键词： Location prediction dynamic Bayesian network majority voting social interaction Instagram

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